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更新时间 2026-11-14 16:55:55
Quantum Flywheel — BlueQubit's $150K Sponsorship Program
GRANT PROGRAM · OPEN CALL 2026
AI agents, cloud compute, quantum hardware access, and a quantum-native development environment — put into the hands of researchers with promising ideas and no budget to test them. Frontier AI has collapsed the path from hypothesis to working experiment from months to days. Compute access is now the bottleneck. Quantum Flywheel removes it.
PROGRAM BUDGET · BLUEQUBIT + AWS COMPUTE + IBM QUANTUM COMPUTE + AI TOKENS
DEADLINE · OPEN CALL FOR PROPOSALS
Frontier AI research and coding assistants have removed the classic gates on a quantum result — building the simulation stack, tuning circuits, wrangling tooling. A researcher can now draft a full experimental pipeline in an afternoon.
That pipeline still stalls the moment it needs real QPU time and real classical horsepower behind it. Quantum Flywheel closes the gap: AI-accelerated ideation, plus AWS compute, plus BlueQubit's quantum R&D platform, which itself runs on AWS.
Intended outcome: publishable results and open artifacts — preprints, repositories, and reproducible benchmark data.
WHAT SPONSORED TEAMS GET
Quantum hardware access — QPU time on the latest IBM quantum computers, the core of the program.
Classical compute — Amazon EC2 GPU instances and large 16–120 core CPU machines for simulation, circuit compilation and optimization, and verification runs.
AI agents — frontier research and coding assistants to go from idea to pipeline in days.
BlueQubit platform — a quantum-native development environment with GPU-accelerated simulation, running on AWS.
THREE TRACKS — PROPOSALS COMBINING QUANTUM, AI, AND HEURISTIC METHODS
Quantum advantage on NISQ hardware
Experiments that push today's devices toward results classical methods cannot easily match.
Breaking quantum advantage claims
Adversarial classical simulation of published results — tensor networks, Pauli-path, and heuristic methods.
AI for new error correction codes
Using AI to discover, decode, and evaluate new QEC codes and decoders.
Proposals collected over an 4–5 week window.
The most promising submissions across the three tracks are funded.
Teams get credits, QPU access, and the BlueQubit platform for the 3-month term.
Preprints, open repositories, and reproducible benchmark data.
AI agents, cloud compute, quantum hardware access, and a quantum-native development environment — put into the hands of researchers with promising ideas and no budget to test them. Frontier AI has collapsed the path from hypothesis to working experiment from months to days. Compute access is now the bottleneck. Quantum Flywheel removes it.
PROGRAM BUDGET · BLUEQUBIT + AWS COMPUTE + IBM QUANTUM COMPUTE + AI TOKENS
DEADLINE · OPEN CALL FOR PROPOSALS
Frontier AI research and coding assistants have removed the classic gates on a quantum result — building the simulation stack, tuning circuits, wrangling tooling. A researcher can now draft a full experimental pipeline in an afternoon.
That pipeline still stalls the moment it needs real QPU time and real classical horsepower behind it. Quantum Flywheel closes the gap: AI-accelerated ideation, plus AWS compute, plus BlueQubit's quantum R&D platform, which itself runs on AWS.
Intended outcome: publishable results and open artifacts — preprints, repositories, and reproducible benchmark data.
WHAT SPONSORED TEAMS GET
Quantum hardware access — QPU time on the latest IBM quantum computers, the core of the program.
Classical compute — Amazon EC2 GPU instances and large 16–120 core CPU machines for simulation, circuit compilation and optimization, and verification runs.
AI agents — frontier research and coding assistants to go from idea to pipeline in days.
BlueQubit platform — a quantum-native development environment with GPU-accelerated simulation, running on AWS.
THREE TRACKS — PROPOSALS COMBINING QUANTUM, AI, AND HEURISTIC METHODS
Quantum advantage on NISQ hardware
Experiments that push today's devices toward results classical methods cannot easily match.
Breaking quantum advantage claims
Adversarial classical simulation of published results — tensor networks, Pauli-path, and heuristic methods.
AI for new error correction codes
Using AI to discover, decode, and evaluate new QEC codes and decoders.
Proposals collected over an 4–5 week window.
The most promising submissions across the three tracks are funded.
Teams get credits, QPU access, and the BlueQubit platform for the 3-month term.
Preprints, open repositories, and reproducible benchmark data.
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